Neyman-Pearson Cooperative Spectrum Sensing for Cognitive Radio Networks with Fine Quantization at Local Sensors

Zahabi, Sayed Jalal; Tadaion, Aliakbar; Aı̈ssa, Sonia

doi:10.1109/tcomm.2012.042712.100700

Cited by 32 publications

(9 citation statements)

References 18 publications

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“…In practice, this channel is in general prone to noise, and hence the quantized data may be subjective to distortion during the transmission to the FC. A cooperative spectrum sensing structure was investigated in [29], where local M-level quantized data are reported through distortion channels and fused at the FC. The erroneous channels were molded based on quantization output values.…”

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confidence: 99%

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Gao

Guo

et al. 2015

IEEE Trans. Wireless Commun.

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We consider the problem of distributed detection of a mean parameter corrupted by Gaussian noise in wireless sensor networks, where a large number of sensor nodes jointly detect the presence of a weak unknown signal. To circumvent power/bandwidth constraints, a multilevel quantizer is employed in each sensor to quantize the original observation. The quantized data are transmitted through binary symmetric channels to a fusion center where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. The asymptotic performance analysis of the multibit GLRT detector is provided, showing that the detection probability is monotonically increasing with respect to the Fisher information (FI) of the unknown signal parameter. We propose a quantizer design approach by maximizing the FI with respect to the quantization thresholds. Since the FI is a nonlinear and nonconvex function of the quantization thresholds, we employ the particle swarm optimization algorithm for FI maximization. Numerical results demonstrate that with 2-or 3-bit quantization, the GLRT detector can provide detection performance very close to that of the unquantized GLRT detector, which uses the original observations without quantization.Index Terms-Wireless sensor networks, multilevel quantization, distributed detection, particle swarm optimization algorithm (PSOA).

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confidence: 99%

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Gao

Guo

et al. 2015

IEEE Trans. Wireless Commun.

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“…(i) the fusion problems with continuous observation space using softened hard approach in [21,24,36];…”

Section: Future Research Avenuesmentioning

confidence: 99%

Low-Complexity Optimal Hard Decision Fusion Under the Neyman–Pearson Criterion

Rahaman

Khan

2018

IEEE Signal Process. Lett.

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“…We have previously consid ered this problem in [17] and [18]. Here we briefly go through the derivation procedure and will highlight the results.…”

Section: Neyman-pearson Fusionmentioning

confidence: 99%

“…P u 0 'Ho (16) It is clear that, having obtained the PMF of log L (u l1io), T is set to satisfy the Pra requirement, i.e., pia = L P(logL(u)l1io) . (17) logL(u»T Hence, the performance of the N-P fusion rule can also be obtained as follows, pi = L P(1ogL(u)l1id · (18) logL(u»T However, since log L(ul1io), is a Discrete Random Variable, its Cumulative Distribution Function (CDF) is not continuous, therefore the above equations are applicable only for some specific rates of False Alarm (would lead to a number of non randomized performance points in the (Pfa, Pd) plain.). Hence, in order to determine the N-P fusion rule and its performance for arbitrary pia, Randomization is required [19], i.e, the N-P fusion decision rule is given by, In this section we evaluate the performance of our proposed scheme through simulation.…”

Section: Neyman-pearson Fusionmentioning

confidence: 99%

Random matrix cooperative spectrum sensing for clustered sensors using Neyman-Pearson fusion

Zahabi¹,

Tadaion²,

Rashvand³

2010

IET International Conference on Wireless Sensor Network 2010 (IET-WSN 2010)

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In this paper we use a new approach to applying the random matrix properties of cognitive radio to spectrum sensing in cognitive radio for clustered sensors, where the Secondary User (SU) sensors within a cluster are assumed to be experiencing the same noise variance and the same Primary User (PU) Signal to Noise Ratio (SNR). Pointing out some recent works on the application of Random Matrix Theory (RMT) in spectrum sensing, we suggest slight but effective changes to the previously mentioned detection strategies, which enables us to examine the idea more comprehensively from a detection theory point of view.We apply the proposed detection strategy as our spectrum sensing scheme within clusters, we then assume to have a Neyman Pearson Fusion Center where the cluster decisions are combined to obtain the final decision as our spectrum sensing. Simulation results show that with no prior knowledge about the PU signal or the noise distribution, our proposed scheme performs quite desirably.

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Neyman-Pearson Cooperative Spectrum Sensing for Cognitive Radio Networks with Fine Quantization at Local Sensors

Cited by 32 publications

References 18 publications

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Quantizer Design for Distributed GLRT Detection of Weak Signal in Wireless Sensor Networks

Low-Complexity Optimal Hard Decision Fusion Under the Neyman–Pearson Criterion

Random matrix cooperative spectrum sensing for clustered sensors using Neyman-Pearson fusion

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